buaaplayer

buaaplayer's Stars

• hasanirtiza/Pedestron

  [Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

  Language:Python687159

• robmarkcole/fire-detection-from-images

  Detect fire in images using neural nets

  Language:Jupyter Notebook38475

• weiaicunzai/pytorch-cifar100

  Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

  Language:Python4.2k1.2k

• buaaplayer/deep_learning_object_detection

  A paper list of object detection using deep learning.

  1

• hoya012/deep_learning_object_detection

  A paper list of object detection using deep learning.

  Language:Python11.3k2.8k

• qixuxiang/Pytorch_Lightweight_Network

  Lightweight Networks such as MobileNet, ShuffleNet and ThunderNet implemented in Pytorch

  Language:Python7216

• princeton-vl/pose-hg-train

  Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"

  Language:Jupyter Notebook575185

• liux0614/yolo_nano

  Unofficial implementation of yolo nano

  Language:Python25371

• DayBreak-u/Thundernet_Pytorch

  Implementation Thundernet

  Language:Python27670

• MichaelBeechan/ThunderNet-Review

  Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

  2611

• buaaplayer/PyTorch-YOLOv3_eriklindernoren

  Minimal PyTorch implementation of YOLOv3

  1

• extreme-assistant/CVPR2024-Paper-Code-Interpretation

  cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

  12.4k2.3k

• xingyizhou/CenterNet

  Object detection, 3D detection, and pose estimation using center point detection:

  Language:Python7.3k1.9k

• eriklindernoren/PyTorch-YOLOv3

  Minimal PyTorch implementation of YOLOv3

  Language:Python7.3k2.6k

• johri-lab/Automatic-leaf-infection-identifier

  Automatic detection of plant diseases

  Language:Python236161

• yangxudong/deeplearning

  深度学习相关的模型训练、评估和预测相关代码

  Language:Python1k377

• sunwantong/China-Merchants-Bank-credit-card-Cente-User-purchase-forecast

  招商银行信用卡中心 消费金融场景下的用户购买预测 rank1

  Language:Python20471

• facebookresearch/VideoPose3D

  Efficient 3D human pose estimation in video using 2D keypoint trajectories

  Language:Python3.7k750

• wszqkzqk/deepin-wine-ubuntu

  Deepin Wine for Ubuntu/Debian

  Language:C6k870

• kenshohara/3D-ResNets-PyTorch

  3D ResNets for Action Recognition (CVPR 2018)

  Language:Python3.9k930

• pyg-team/pytorch_geometric

  Graph Neural Network Library for PyTorch

  Language:Python21.1k3.6k

• mdeff/cnn_graph

  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

  Language:Jupyter Notebook1.3k390